A review of domain adaptation without target labels

Domain adaptation has become a prominent problem setting in machine learning and related fields. This review asks the question: how can a classifier learn from a source domain and generalize to a target domain? We present a categorization of approaches, divided into, what we refer to as, sample-based, feature-based and inference-based methods. Sample-based methods focus on weighting individual observations during training based on their importance to the target domain. Feature-based methods revolve around on mapping, projecting and representing features such that a source classifier performs well on the target domain and inference-based methods incorporate adaptation into the parameter estimation procedure, for instance through constraints on the optimization procedure. Additionally, we review a number of conditions that allow for formulating bounds on the cross-domain generalization error. Our categorization highlights recurring ideas and raises questions important to further research.Comment: 20 pages, 5 figure

Robust subspace learning for static and dynamic affect and behaviour modelling

Machine analysis of human affect and behavior in naturalistic contexts has witnessed a growing attention in the last decade from various disciplines ranging from social and cognitive sciences to machine learning and computer vision. Endowing machines with the ability to seamlessly detect, analyze, model, predict as well as simulate and synthesize manifestations of internal emotional and behavioral states in real-world data is deemed essential for the deployment of next-generation, emotionally- and socially-competent human-centered interfaces. In this thesis, we are primarily motivated by the problem of modeling, recognizing and predicting spontaneous expressions of non-verbal human affect and behavior manifested through either low-level facial attributes in static images or high-level semantic events in image sequences. Both visual data and annotations of naturalistic affect and behavior naturally contain noisy measurements of unbounded magnitude at random locations, commonly referred to as ‘outliers’. We present here machine learning methods that are robust to such gross, sparse noise. First, we deal with static analysis of face images, viewing the latter as a superposition of mutually-incoherent, low-complexity components corresponding to facial attributes, such as facial identity, expressions and activation of atomic facial muscle actions. We develop a robust, discriminant dictionary learning framework to extract these components from grossly corrupted training data and combine it with sparse representation to recognize the associated attributes. We demonstrate that our framework can jointly address interrelated classification tasks such as face and facial expression recognition. Inspired by the well-documented importance of the temporal aspect in perceiving affect and behavior, we direct the bulk of our research efforts into continuous-time modeling of dimensional affect and social behavior. Having identified a gap in the literature which is the lack of data containing annotations of social attitudes in continuous time and scale, we first curate a new audio-visual database of multi-party conversations from political debates annotated frame-by-frame in terms of real-valued conflict intensity and use it to conduct the first study on continuous-time conflict intensity estimation. Our experimental findings corroborate previous evidence indicating the inability of existing classifiers in capturing the hidden temporal structures of affective and behavioral displays. We present here a novel dynamic behavior analysis framework which models temporal dynamics in an explicit way, based on the natural assumption that continuous- time annotations of smoothly-varying affect or behavior can be viewed as outputs of a low-complexity linear dynamical system when behavioral cues (features) act as system inputs. A novel robust structured rank minimization framework is proposed to estimate the system parameters in the presence of gross corruptions and partially missing data. Experiments on prediction of dimensional conflict and affect as well as multi-object tracking from detection validate the effectiveness of our predictive framework and demonstrate that for the first time that complex human behavior and affect can be learned and predicted based on small training sets of person(s)-specific observations.Open Acces

Feature Space Augmentation: Improving Prediction Accuracy of Classical Problems in Cognitive Science and Computer Vison

The prediction accuracy in many classical problems across multiple domains has seen a rise since computational tools such as multi-layer neural nets and complex machine learning algorithms have become widely accessible to the research community. In this research, we take a step back and examine the feature space in two problems from very different domains. We show that novel augmentation to the feature space yields higher performance. Emotion Recognition in Adults from a Control Group: The objective is to quantify the emotional state of an individual at any time using data collected by wearable sensors. We define emotional state as a mixture of amusement, anger, disgust, fear, sadness, anxiety and neutral and their respective levels at any time. The generated model predicts an individual’s dominant state and generates an emotional spectrum, 1x7 vector indicating levels of each emotional state and anxiety. We present an iterative learning framework that alters the feature space uniquely to an individual’s emotion perception, and predicts the emotional state using the individual specific feature space. Hybrid Feature Space for Image Classification: The objective is to improve the accuracy of existing image recognition by leveraging text features from the images. As humans, we perceive objects using colors, dimensions, geometry and any textual information we can gather. Current image recognition algorithms rely exclusively on the first 3 and do not use the textual information. This study develops and tests an approach that trains a classifier on a hybrid text based feature space that has comparable accuracy to the state of the art CNN’s while being significantly inexpensive computationally. Moreover, when combined with CNN’S the approach yields a statistically significant boost in accuracy. Both models are validated using cross validation and holdout validation, and are evaluated against the state of the art

Connecting Subspace Learning and Extreme Learning Machine in Speech Emotion Recognition

Speech Emotion Recognition (SER) is a powerful tool for endowing computers with the capacity to process information about the affective states of users in human-machine interactions. Recent research has shown the effectiveness of graph embedding based subspace learning and extreme learning machine applied to SER, but there are still various drawbacks in these two techniques that limit their application. Regarding subspace learning, the change from linearity to nonlinearity is usually achieved through kernelisation, while extreme learning machines only take label information into consideration at the output layer. In order to overcome these drawbacks, this paper leverages extreme learning machine for dimensionality reduction and proposes a novel framework to combine spectral regression based subspace learning and extreme learning machine. The proposed framework contains three stages - data mapping, graph decomposition, and regression. At the data mapping stage, various mapping strategies provide different views of the samples. At the graph decomposition stage, specifically designed embedding graphs provide a possibility to better represent the structure of data, through generating virtual coordinates. Finally, at the regression stage, dimension-reduced mappings are achieved by connecting the virtual coordinates and data mapping. Using this framework, we propose several novel dimensionality reduction algorithms, apply them to SER tasks, and compare their performance to relevant state-of-the-art methods. Our results on several paralinguistic corpora show that our proposed techniques lead to significant improvements

Affective Facial Expression Processing via Simulation: A Probabilistic Model

Understanding the mental state of other people is an important skill for intelligent agents and robots to operate within social environments. However, the mental processes involved in `mind-reading' are complex. One explanation of such processes is Simulation Theory - it is supported by a large body of neuropsychological research. Yet, determining the best computational model or theory to use in simulation-style emotion detection, is far from being understood. In this work, we use Simulation Theory and neuroscience findings on Mirror-Neuron Systems as the basis for a novel computational model, as a way to handle affective facial expressions. The model is based on a probabilistic mapping of observations from multiple identities onto a single fixed identity (`internal transcoding of external stimuli'), and then onto a latent space (`phenomenological response'). Together with the proposed architecture we present some promising preliminary resultsComment: Annual International Conference on Biologically Inspired Cognitive Architectures - BICA 201

An original framework for understanding human actions and body language by using deep neural networks

The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour. By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way. These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively. While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements; both are essential tasks in many computer vision applications, including event recognition, and video surveillance. In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided. The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements. All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods